Maintaining consistent quality levels is important in the production and manufacturing of goods. Traditionally, quality levels are audited or graded by inspectors to ensure that products meet or exceed a target grade.
For example, in the production of French Fries, an inspector removes a sample from a production line and transports the sample to a lab. At the lab, the inspector examines each piece to determine if it is defective. For less desirable or defective pieces, the inspector classifies the piece by examining any less desirable aspects or defects that are present on the piece according to a pre-determined criterion. In this example, the less desirable or defective piece can be classified as a critical, major, or minor. The inspector scores the piece by assigning a value according to its defect classification, which in this case a value of three is assigned for critical defects, a value of two is assigned for major defects, and a value of one is assigned for minor defects. When all of the pieces have been inspected, the inspector adds the values together to determine the grade for the sample.
In many cases, the grade or quality measurement for the sample is recorded on a control chart and compared with the target grade or quality level or associated control limits to determine if any process parameters should be modified.
There are many process parameters available to operational personnel that affect the grade or quality level of a product stream. Many processors utilize machine vision sorting equipment to remove defective pieces from the product stream to meet grade. Often, an operator will, over the course of a shift of operation, modify parameters on the sorting equipment to respond to changing incoming product stream quality levels. For example, if raw product has a high incoming defect level, an operator may find it necessary to change parameters on the sorting equipment to more aggressively remove defective product. Often, an operator will modify a defect area size threshold, making it smaller for more aggressive removal of defects and larger for less aggressive removal. Such adjustments, though, are often based on latent information which may not reflect current quality conditions which may vary significantly over a short period of time.
State-of-the-art sorting equipment is effective in removing defects from incoming product streams even at high defect rates. In fact, sorting equipment is selected so that it is capable of providing a pass product stream having a grade that is better than the target grade in even worst case incoming product stream defect rates. The sorting equipment is often adjusted to attempt to remove all less desirable objects or defects, especially major and critical defects.
So, while a processor is able to keep the processing line grade below the target grade and better than the quality target, the overall yield of the processing line is not optimized because more defective or less desirable pieces as well as good pieces are removed which could have been passed while still meeting the quality goals of the processor and customer.
One example of a system designed to address at least a portion of this problem is an integrated food sorting and analysis apparatus is taught in U.S. Pat. No. 5,526,437 and incorporated by reference herein. Here, an upstream camera is positioned to view a product stream, and operable to drive a product diverter based on observed object characteristics and automated sorting logic. The apparatus also includes a downstream camera positioned to view the product stream after it has passed the product diverter. A data processor is responsive to both the upstream camera and the downstream camera to periodically examine samples of food objects and to calculate upstream and downstream quality statistics. In addition, the data processor also calculates settings for the automated sorting logic based upon the calculated quality statistics. While notable in its innovation, the apparatus described above suffers from a number of problems including the requirement of a downstream camera which significantly increases the complexity and cost of the system making it unfeasible in many situations. In addition, the information gleaned by the downstream camera from a batch sample of food objects does not necessarily reflect the objects that are currently being processed by the upstream camera making any subsequent adjustment prone to error that is a function of a real-time, time-varying, and non-homogenous quality distribution for objects in the product stream.
What is needed then is a machine that is effective in maintaining a quality level of a pass or output product stream at a quality level that follows and is slightly better than the target quality level, thereby maximizing yield.